code: def get_prompt_slice(
      gt_path,
      max_sample_length,
      min_sample_length,
      sample_rate,
      is_eval=False,
  ):
      rel_clip = load_audio(gt_path, sample_rate)
      # if eval uses a middle size sample when it is possible to be more reproducible
      if is_eval:
          sample_length = int(
              (min_sample_length + max_sample_length) / 2
          )
      else:
          sample_length = random.randint(
              min_sample_length, max_sample_length
          )
      gap = rel_clip.shape[-1] - sample_length
      if gap < 0:
          sample_length = rel_clip.shape[-1] // 2
      gap = rel_clip.shape[-1] - sample_length
  
      # if eval start always from the position 0 to be more reproducible
      if is_eval:
          rand_start = 0
      else:
          rand_start = random.randint(0, gap)
  
      rand_end = rand_start + sample_length
      rel_clip = rel_clip[:, rand_start:rand_end]
      rel_clip = F.pad(
          rel_clip, pad=(0, max_sample_length - rel_clip.shape[-1])
      )
      cond_idxs = [rand_start, rand_end]
      return rel_clip, rel_clip.shape[-1], cond_idxs
  
file_path: TTS/TTS/tts/layers/xtts/trainer/dataset.py
imports: import os
  import random
  import sys
  import torch
  import torch.nn.functional
  import torch.utils.data
  from TTS.tts.models.xtts import load_audio
label_desc: Use image node_id 2 for calling a global function with example usage: get_prompt_slice(gt_path, max_sample_length, min_sample_length, sample_rate, is_eval) and returns: rel_clip,  rel_clip,  cond_idxs